The decreasing fossil feedstocks and increasing CO2 emissions lead to the development of alternative resources for energy. Solar energy has become an increasingly popular alternative resource in recent years. The International Energy Agency report forecasts that renewable energy will contribute to almost 30% of power demand in 2023 up from 24% in 2017. Amongst all the renewable energy sources the contribution of solar energy was reported to be only 4%. Though this number is rising, it is rising slowly due to the expensive cost of fabricating current silicon solar panels. Perovskites, a new class of solar cells present the possibility of a less expensive solar harvester through the use of low-cost materials and cheaper fabrication techniques. Discovered in 1839 by Gustav Rose, the mineral CalciumTitanate was the first perovskite to be defined with the general chemical formula of ABX3, where A and B are cations of very different sizes, and X is an anion. All chemicals which fit this structure can be defined as a perovskite if they also adhere to a tolerance factor termed as the Goldschmidt tolerance factor. It has been estimated that the theoretical conversion efficiency of perovskite cells is almost double the efficiency of silicon solar cells. Current perovskite cell technology focuses on a specific perovskite crystal known as methylammonium-lead-halide. Nevertheless, the efficiency of these cells has risen over the last two years to 22%, there are a lot of more possible combinations of elements and/or compounds that could result in the formation of other perovskite crystals, and probably, give higher conversion efficiencies closer to the possible theoretical conversion. In order to synthesize perovskite crystals which can follow the Goldschmidt’s tolerance factor, and at the same time have a reasonable cost, we formulated an optimization problem as a mixed-integer programming (MIP). These problems were solved using integer programming solvers, CPLEX and BINT in GAMS and MATLAB respectively. Since we had to choose from a number of possible combinations of each cation and anion, we modeled our problem using binary programming, where ions selected were assigned a value of 1 and those deselected were assigned a 0 value. Our work allows for a selection of materials based on stability and cost, two of the most significant criteria for large scale manufacturing of solar cells.
No datasets are available for this submission.
No keywords are available for this submission